Bat algorithm (BA) has the advantage of fast convergence, but there is still room for improvement in accuracy and stability of solution. An efficient and robust fusion bat algorithm (ERFBA) is proposed to overcome these defects. In the population reconstruction, an effective diversity population (EDP) is reconstructed by designing a multi-strategy opposition-based learning with disturbance. In the exploration, an adaptive constraint step whale optimization algorithm is presented to obtain the promising regions with fewer blind spots by exploring EDP. In the exploitation, we design a new BA local search strategy by novel combination between dynamic regulation and Cauchy mutation to get accurate and stable solution. Numerous experiments show that ERFBA has remarkable advantages in accuracy and stability for many high dimension, unimodal and multimodal problems. Moreover, the proposed algorithm is further tested and applied in areas of intelligent data analysis and intelligent design. The results show that the overall performance of the proposed ERFBA is better than other existing algorithms.
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